Tools for Thought on the Farm: Rethinking How We Build and Use Agricultural Software
Over the past few years, I've spent a lot of time thinking about how farmers and their advisors make decisions. Not the tidy kind you'd find in a management textbook, but the messy, high-stakes, context-rich decisions that shape a growing season. The more time I spend looking at the farm management software we've built to "support" those decisions, the more I'm convinced we've misunderstood the problem.
It's not that these tools are useless, they do a good job of tracking inputs, mapping paddocks, and producing compliance reports. But I keep returning to the same feeling: most farm management systems aren't actually helping people think. They help them record. They help them report. But they don't scaffold the kind of cognitive work that good farming systems demand, the judgment, the pattern recognition, the memory of past seasons, the "what ifs" and the "I wonder whys".
I've come to believe we need a new kind of software in agriculture. Not just faster dashboards or smarter predictions, but something deeper: tools for thought. The idea isn't new. In fact, it goes back to early computing visionaries like Douglas Engelbart, who believed that the real promise of computers wasn't automation, but augmentation, using technology to extend human intelligence. With all the recent advances in computing over the last couple of years, I find that idea more relevant now than ever.
What if our software helped us think like better farmers?
The field of "tools for thought" has been quietly gaining traction in other domains, research, writing, knowledge work, and it's grounded in a simple question: how can we design systems that make thinking easier, clearer, and more powerful?
In agriculture, this matters because farming is cognitively demanding. It involves long feedback loops and high uncertainty. It requires balancing intuition with evidence, local knowledge with global signals, experience with adaptation. And it's exactly the kind of domain where digital tools could do more than automate. They could become collaborators in the thinking process.
Right now, most farm software assumes that better decisions will come from better data.
Right now, most farm software assumes that better decisions will come from better data. But I don't think that's the whole story. Farmers and their advisors don't just need more data, they need tools that help them make sense of it, draw meaningful connections, and revisit the reasoning behind past decisions. That's the kind of support that actually improves judgment over time.
LLMs and the promise of conversational thinking
I've been exploring how large language models (LLMs), like GPT-4 and others, might play a role here. Not just as tools that spit out answers, but as conversational tools that support and challenge human reasoning.
Imagine a farmer chatting with an assistant that remembers last season's conditions, understands their paddock history, knows what crops are in the ground and what's coming up. Not a clunky chatbot that recites weather forecasts, but a genuinely thoughtful companion that can say, "You had a similar soil moisture profile in 2020, remember what happened with your early canola planting then?"
Or: "You mentioned concerns about disease pressure building. Would you like to review what you tried last time you saw this pattern?"
This isn't just about surfacing data, it's about surfacing thinking. The model becomes a sort of reflective mirror, helping the user retrieve forgotten insights, question assumptions, and play out scenarios. And crucially, it does this in plain language, through dialogue, though imagery, the oldest and most human tools for thought we have.
I don't believe LLMs will replace agronomists or seasoned growers. But I do believe they can become invaluable thinking partners, not telling us what to do, but helping us explore our options more clearly, reason through them more deliberately, and learn more effectively from what's already happened.
Memory, meaning, and making connections
One of the persistent challenges I see with today's farm software is that it doesn't remember the things that matter. It remembers that something was sprayed on a certain paddock, sure, but beyond some short notes, not why. It doesn't remember the trade-offs you were considering, or the advice you got, or what your gut told you versus what the model you used predicted.
A true tool for thought would help capture and connect all of this. Not in a time sucking, over bearing way, but as a natural byproduct of using the system. Every note, decision, observation, each one becomes part of a living knowledge base that you can return to, search through, reflect on. The goal isn't just to document the season. It's to build a cognitive archive, one that helps you think better next time.
The goal isn't just to document the season. It's to build a cognitive archive, one that helps you think better next time.
And this, to me, is where AI gets exciting. A language model trained not just on global knowledge but on your farm, your patterns, your decisions. A system that can surface insights you've forgotten, show you what's changed since last time, help you simulate the season ahead, and do it in a way that feels intuitive, grounded, and genuinely useful.
Designing for thought, not just data
Of course, this isn't just about technology. It's about the design of the entire user experience. Tools for thought research tells us that good thinking tools have a few key qualities: they encourage linking, not just listing. They make it easy to externalise and revisit your own reasoning. They support exploration and reflection, not just execution. And they fit the grain of the user's mind, not just the needs of the database or the compliance form.
In agriculture, that means designing systems that are locally aware, temporally rich, and able to operate with ambiguity. It means supporting the tacit knowledge farmers already have, not trying to replace it with rigid rules or oversimplified models. And it means valuing explainability and dialogue over black-box answers.
There's already some promising work in this space, prototypes that use graph-based memory, local-first AI assistants, tools that let you link paddock observations with climate data, treatments, and outcomes. But I think we're still at the very beginning of what's possible. I think very soon the farm management systems of today will become redundent.
Toward a Smarter Kind of Farm Management
What if farm management software made you feel smarter every time you used it?
That's the vision that excites me. A thinking environment that helps farmers and agronomists build better mental models over time. A tool that doesn't just show what happened but helps explain why, and helps prepare for what might come next.
This won't come from just layering AI onto existing tools. It requires rethinking the foundation: designing systems that are truly in service of human thought, not just data collection. That understand context, support reasoning, and evolve alongside the people who use them.
It's time, I think, to move beyond dashboards (they're dead) and compliance, and start building cognitive companions for agriculture because when the work is this important, and the challenges this complex, thinking well isn't a luxury. It's a necessity.